Hybrid studies allow investigators to simultaneously study an intervention effectiveness outcome and an implementation research outcome. In particular, type 2 hybrid studies support research that places equal importance on both outcomes rather than focusing on one and secondarily on the other (i.e., type 1 and type 3 studies). Hybrid 2 studies introduce the statistical issue of multiple testing, complicated by the fact that they are typically also cluster randomized trials. Standard statistical methods do not apply in this scenario. Here, we describe the design methodologies available for validly powering hybrid type 2 studies and producing reliable sample size calculations in a cluster-randomized design with a focus on binary outcomes. Through a literature search, 18 publications were identified that included methods relevant to the design of hybrid 2 studies. Five methods were identified, two of which did not account for clustering but are extended in this article to do so, namely the combined outcomes approach and the single 1-degree of freedom combined test. Procedures for powering hybrid 2 studies using these five methods are described and illustrated using input parameters inspired by a study from the Community Intervention to Reduce CardiovascuLar Disease in Chicago (CIRCL-Chicago) Implementation Research Center. In this illustrative example, the intervention effectiveness outcome was controlled blood pressure, and the implementation outcome was reach. The conjunctive test resulted in higher power than the popular p value adjustment methods, and the newly extended combined outcomes and single 1-DF test were found to be the most powerful among all of the tests.